Jamshed Butt

Software Engineer (AI)

Pikes Soft • Full time

Nov 2025 - Present • 11 mos

I specialize in architecting, building, and deploying production-grade Agentic AI workflows, multi-agent systems, and scalable backend microservices using LangGraph, OpenAI Agent SDK, NestJS, and FastAPI. 🚀 Core Architecture & Responsibilities • Build and deploy Agentic AI workflows, multi-agent systems, and scalable backend microservices. • Manage containerized infrastructure across Google Cloud Platform (Cloud Run, Cloud SQL, Memorystore) and AWS EC2 using Docker. 🔹 Key Projects SubRedditorX — Autonomous Android AI Agent • Built an autonomous mobile agent using LangGraph StateGraph on Android Emulator with step-by-step reasoning and dynamic screen hierarchy analysis. • Automated Reddit posts, comments, voting, and content extraction with self-healing recovery for popups and UI changes. • Integrated GPT-4o for content generation and deployed FastAPI on AWS EC2 with Docker. WYNA — AI Fitness & Multi-Agent Platform • Developed modular NestJS/TypeScript backend with JWT, RBAC, and REST APIs. • Built a multi-agent chatbot using OpenAI Agent SDK, domain guardrails, and Pinecone RAG. • Cached sessions with Google Cloud Memorystore (Redis) and deployed on Cloud Run with Cloud SQL (PostgreSQL). AI Car Service Booking Assistant • Built an agentic booking assistant using GPT-4o on Azure Agent Platform. • Integrated MCP tools for real-time shop availability, location-based routing, and appointment lifecycles. AR-Based 3D Medical Processing & Predictive ML • Built a React + FastAPI platform orchestrating 3D Slicer and Blender pipelines on Railway. • Trained a LightGBM real estate model on 200k+ rows achieving 91% accuracy and deployed inference APIs on AWS. 🛠️ Tech Stack OpenAI Agent SDK • LangGraph • MCP • Azure Agent Platform • NestJS • TypeScript • FastAPI • Python • Pinecone • Redis • Google Cloud Run • AWS EC2 • Docker

AI/ML Engineer

Fabulous • Full time

Jan 2024 - Mar 2026 • 2 yrs 2 mos

💡 Technical Leadership & Engineering Progressed from AI/ML Engineer Intern to Senior Team Lead, driving AI/ML architecture, development, deployment, and engineering leadership. • Led projects across Agentic AI, multi-agent LLMs, Voice AI, Computer Vision, NLP, Predictive ML, and Enterprise RAG. • Architected scalable AI backends, FastAPI microservices, Docker, CI/CD, and cloud infrastructure across AWS, GCP, and Railway. 🚀 Key Projects & Systems • Staron AI: Built an autonomous multi-agent web development platform with OpenAI Agent SDK, collaborative code generation, automated bug fixing, live previews, and Claude/Gemini model routing. • Fleet & AI Hiring: Developed GPT-4.1 resume screening, Vapi AI voice interviews, and Pinecone RAG customer support. • Gala Tab: Built real-time AI guardrails to detect and block PII and confidential data leakage. • B-Master: Built a multi-agent SaaS using LangChain, LangGraph, and Pinecone with document parsing, transcription, image analysis, semantic retrieval, Stripe billing, and natural-language database queries. • Tax & Health AI: Developed GPT-4o tax-form analysis, MongoDB tax strategy agents, Pinecone RAG, and health-report analysis using OCR with GPT-4.1 agents for personalized plans. • Computer Vision & ML: Built vehicle recognition using Roboflow, YOLOv11, and OpenCV; LightGBM real-estate model trained on 200K+ rows with 91% accuracy; developed readmission and dengue forecasting using ML and time-series methods. • Automation: Built layout-preserving PDF translation using OCR and Google Translate API, plus nutrition assistants and Streamlit ML dashboards. 🛠️ Tech Stack OpenAI Agent SDK, LangGraph, LangChain, GPT-4o/4.1, Claude, Gemini, Vapi AI, RAG, Pinecone, FastAPI, Python, Docker, NestJS, AWS, GCP, Railway, MongoDB, YOLOv11, OpenCV, Scikit-Learn, LightGBM, Streamlit, OCR, NLP, ARIMA/SARIMA, CI/CD.

Machine Learning Engineer Intern

Technocolabs Softwares • Full time

Mar 2023 - Jun 2023 • 3 mos

Engaged in a wide range of tasks contributing to the success of various projects while gaining in-depth exposure to teamwork and technical problem-solving. Responsibilities included conducting Exploratory Data Analysis (EDA), performing Data Analysis, implementing Feature Engineering, and developing and deploying machine learning models. Key accomplishments: ✅ Played a critical role in project teams by conducting comprehensive EDA, uncovering critical insights, and applying findings to enhance feature engineering processes. ✅ Gained proficiency in deploying machine learning models, ensuring seamless integration into production environments and enabling data-driven solutions. ✅ Collaborated with cross-functional teams, effectively communicating complex technical concepts to ensure smooth coordination and knowledge exchange. ✅ Acquired valuable industry knowledge, fostering a strong understanding of the dynamic nature of data science and machine learning applications. This experience provided significant growth in technical expertise, including Python, EDA, Feature Engineering, Machine Learning Algorithms, Model Deployment, and Data Visualization using tools like Streamlit.